Problem Statement

We have been tasked with developing an update on Monkeypox (MPX) for the leadership of our state health department. MPX is a rare disease caused by infection with the monkeypox virus. As there has been a recent MPX outbreak in Europe, we will be analyzing their data to prepare a response for an outbreak here.

We will be investigating how case rates changed over time in four different regions in Europe. We will also look at certain demographics and MPX rates by country. We will look at whether gender (male and female) affects case rates, as well as age (people 85 and older, and people younger than 85).

Methods

We used four different datasets to complete this report:

  1. Monkeypox data from the European Centre for Disease Prevention and Control (ECDC)
  2. European population denominators data from Eurostat
  3. World regions and country codes from the International Organization for Standardization (ISO) 3166 (Geneva, Switzerland)
  4. Demographic data from the European Statistical System’s 2011 European census data

From the ISO 3166 dataset, we filtered by European region followed by selecting the four European subregions and country codes. We then used the substring function to extract only the two-letter country code contained within the last two characters of the ISO 3166 alpha-2 column, matching country-code formats with the ECDC MPX & Eurostat population denominators datasets in preparation for joining. The ECDC MPX dataset spanned dates from 2022-05-09 to 2022-08-23 and included the reported date of the number of confirmed monkey pox cases by country and the surveillance source of data collection. From this dataset, we filtered by date, country code and confirmed number of MPX cases. To obtain the population denominators used in our calculation of weekly MPX case rates, we incorporated Eurostat’s European population denominators dataset, which included yearly population data of European countries from 2011 to 2022 based on the total population residing in that country as of January 1st of each year. We used these three subsetted datasets for both investigations, MPX case rates by European subregions and MPX case rates by demographic factors.

To calculate MPX case rates by European subregion, we first filtered the European population dataset by the year 2022 and then selected country code as well as total population. We filtered by the year 2022 because the ECDC MPX dataset only included MPX cases from this year. We then inner joined the European population denominators, ECDC MPX and ISO3166 subsetted datasets together by country code. There were no discrepancies between datasets, therefore, no missing values after joining. Our final aggregated dataset contained five columns: European sub region, country code, confirmed number of monkeypox cases and population denominators data. After grouping by the four European subregions, we created a new MPX case rate by ten million variable by dividing the sum of total MPX cases by the total population in each corresponding subregion (Figure 1).

The European Statistical System’s 2011 European census data contained EU country codes, sex at birth, age ranges, employment status (CAS), education status, population of locality of residence and the number of people in each strata. Since we were only interested in observing possible trends in European countries’ MPX case rates when stratified by the female gender and people aged 85 years and older, we created a new dataframe grouping by country code and filtering for the female sex and another dataframe grouping by country code and filtering for people aged 85 years and older per country. These two dataframes were joined by country code, and the percentages of each respective demographic per country was calculated. We then joined our aggregated census data with our data frame containing MPX case rates by European country (Table 1). Trends in European MPX case rates by people 85 years of age and older and female gender can be seen in Figure 2 and Figure 3, respectively.

Results

Figure 1

All four regions experienced a similar chronological trend from May to August where May had the lowest case rates and July had the highest case rates. Between the four regions, there was a consistent ordering of case rates at each time point: Eastern Europe had the lowest rate, followed by Northern Europe, Western Europe, and Southern Europe. One interesting anomaly is that Northern Europe does not appear to experience as significant of a downward trend from July to August that the other regions experienced.
Note: MPX case rates are per ten million people.

Table 1

2011 Census Rates versus 2022 Monkeypox Rates by Country
Country Code % 85 and Older % Female Case Rate per Million
AT 1.58 49.79 0.2497757
BE 1.57 49.42 0.5600969
BG 1.01 50.07 0.0056785
CZ 1.01 49.36 0.0378501
EE 1.19 52.64 0.0728996
FI 1.43 50.38 0.0384973
FR 1.74 50.33 0.4134357
HR 1.00 50.09 0.0625712
HU 1.15 50.69 0.0641303
IE 0.88 48.60 0.2455963
LU 1.12 48.73 0.7070232
LV 1.08 53.17 0.0207036
MT 1.08 46.36 0.5777114
PL 0.94 49.68 0.0311985
PT 1.51 50.91 0.7596644
RO 0.98 49.18 0.0178487
SI 1.14 48.77 0.1981206
SK 0.76 49.54 0.0214372

From this table view, a trend does not seem to appear between a country’s percent of the population that is female and its monkeypox rate. However, a positive trend is possible between percent of the population that is 85 and older and that country’s monkeypox case rate.

Figure 2

Plotting the percent of people 85 years and older against the MPX case rates in European countries shows an upward trend.

Figure 2

Plotting the percent of European countries that are female against the MPX case rates in European countries shows a downward trend.

Discussion